Mathematics and Statistical Science BSc

3 Years On Campus Bachelors Program

University College London

Program Overview

This programme combines rigorous training in pure and applied mathematics with a comprehensive grounding in statistical science, making it ideal for students who enjoy logical reasoning and data‑driven insight. It suits someone aiming to build both strong theoretical maths skills and practical statistical expertise, preparing them for roles in data science, actuarial work or further study.

Curriculum structure:

Year 1:
In the first year students build foundational skills in core mathematics through modules such as Analysis 1, Algebra 1, Mathematical Methods 1, while also beginning the statistics side via Introduction to Probability and Statistics. The year also introduces computing skills and practical statistical work (e.g., Introduction to Practical Statistics).

Year 2:
In the second year the mathematics component deepens into subjects like Algebra 2, Analysis 3 (Complex Analysis), Analysis 4 (Real Analysis), and the statistics side continues with modules such as Probability and Inference, Linear Models and the Analysis of Variance, Introduction to Applied Probability and Computing for Practical Statistics. This gives you both the theoretical and applied arms of the discipline.

Year 3:
In the final year students are given much broader choice: advanced mathematics topics can include Geometry and its Applications, Combinatorics, Financial Mathematics, Mathematical Biology, while on the statistics side there are theoretical and practical modules (for example advanced statistical inference, optional modules in social statistics or optimisation). Students may also select an outside‑department option. This flexibility enables tailoring the final year to career goals.

Focus areas:
Mathematics (pure and applied) and Statistical Science (probability, inference, modelling, computing/data techniques).

Learning outcomes:
Graduates will be able to think rigorously about abstract mathematical structures; apply statistical methods to real‑world data; program and compute with applied statistical tools; argue quantitatively and critically; and pursue careers in data‑science, actuarial, finance, research or further academic study.

Professional alignment (accreditation):
The degree is accredited by the Royal Statistical Society for students who first enrol between September 2023 and September 2028.

Reputation (employability rankings):
UCL’s Mathematics department holds strong international standing (e.g., ranked 6th in the UK in QS by Subject for Mathematics) and the programme reports graduates working with organisations such as Deloitte, Goldman Sachs, JP Morgan, Amazon, Deutsche Bank.
This combination of math + stats ensures highly employable skills in an increasingly data‑driven job market.

Experiential Learning (Research, Projects, Internships etc.)

Students on the Mathematics and Statistical Science programme engage in a rich blend of pure mathematics and statistics from day one. They do much more than attend lectures: they work through problem‑sheets, participate in computer labs, and tackle group projects. For instance, the programme includes an Introduction to Practical Statistics module and Computing for Practical Statistics in year two. Students access UCL’s computing facilities and mathematical‑science resources, combining theoretical reasoning with practical data‑driven work. Lectures and tutorials are reinforced by computer workshops and practical statistics sessions offering real‑world skills.

From this foundation the practical aspects include:

Computing‑based statistics labs: Early years include modules such as Computing for Practical Statistics, enabling students to work with datasets, statistical software and programming.
Group project and presentation: In the second year the curriculum includes a group research‑style project in mathematics, scaffolded with tutorials and presentations — reinforcing independent investigation.
Small‑group tutorials and problem‑sessions: Across both mathematics and statistics work, students engage in tutorials that work through problem‑sheets, discuss solutions and strengthen reasoning, rather than passive listening.
Wide‐ranging optional modules for applied practice: In the third year students choose advanced topics such as Financial Mathematics, Mathematical Biology, Optimisation Algorithms in Operational Research, or Social Statistics. These allow students to tailor application of theory to concrete fields.
Integrated mathematical theory and statistical application: The degree structure ensures that students not only learn the theoretical underpinnings of algebra, analysis and methods but simultaneously engage with statistical inference, linear models and applied probability. This dual‑track builds strong analytical and practical competence.
Access to library and study resources + tutor support: Students benefit from UCL’s rich resources and the departmental support system, including office hours and a Student Mentor scheme for first‑years, allowing deeper clarity and personalised guidance.

Progression & Future Opportunities

Graduates of the Mathematics and Statistical Science BSc at UCL are well-prepared for careers that combine strong mathematical foundations with advanced statistical and analytical skills. Typical career paths include roles in data science, actuarial analysis, quantitative research, and business analytics:

UCL Careers Services: Students benefit from UCL Careers’ dedicated support for statistics and mathematics, including personalised career advice, CV and interview preparation, workshops on data-focused careers, and internship opportunities with leading companies.
Employment stats and salary figures: Over 90% of UCL mathematics graduates are employed or pursuing further study within six months. Starting salaries for statistical and analytical roles typically range between £30,000–£45,000, depending on the sector.
University–industry partnerships: UCL has strong collaborations with tech companies, financial institutions, and research organisations, providing students with project-based experience, placements, and industry networking opportunities.
Long-term accreditation value: This interdisciplinary degree is highly respected internationally, equipping graduates with versatile analytical and problem-solving skills applicable across finance, technology, and research sectors.
Graduation outcomes: Alumni secure roles as data scientists, actuarial analysts, quantitative researchers, business analysts, and statistical consultants.

Further Academic Progression:
Graduates can pursue MSc programs in Statistics, Data Science, Actuarial Science, Financial Mathematics, or Artificial Intelligence. PhD opportunities in Mathematical Statistics, Applied Mathematics, or Computational Modelling are also widely available, alongside professional qualifications in finance, analytics, or risk management.